The present invention is directed to a display processing system which presents advice produced by an expert system in a form particularly useful to the user and, more particularly, to a system which presents the expert advice in graphic form consistent with the graphic depiction of the process control data from which the expert advice is produced.
Within the field of Artificial Intelligence (AI), expert systems, and in particular rulebased expert systems, are becoming increasingly used in industry. These expert systems are normally constructed to emulate the behavior of an expert (or multiple experts) in a particular field, with respect to a particular problem. The intent is often described as attempting to "put Mr. X into a computerized box". These systems often incorporate one of several basic techniques, for example, using a forward chaining rule based system to fire a sequence of rules to arrive at a decision.
Expert systems have been developed for a variety of fields, but tend to share several very common characteristics. In general, expert systems reason based on data representing the world state, using an inferencing method chosen to emulate that of the expert in the field. The systems are intentionally partitioned to separate the knowledge of the state (the knowledge base) and the problem solving routines (the inference engine) which operate on that knowledge base. The intent is to determine an adequate solution to the problem with an accuracy approximating that of a human expert.
Users of expert systems have been found to be either reluctant or too trusting with regards to the advice which is produced by AI programs or modules. Users have been found to be much more comfortable with such a system when they are able to understand the basis for the advice being given. In fact, the acceptance of such a system in the workplace often is determined by the availability and quality of the basis for the advice. This is particularly evident at the knowledge bounds of the expert system, where the expert system is often described as "brittle", that is, where the advice is very likely to be inappropriate due to exceeding the original design scope of the system. It is classically difficult to detect when such an expert system is near its operating limits, and poor advice at those moments tends to reduce the system credibility overall. It is important for the quality of the person-machine interaction that the user be able to judge the soundness of the advice being given.
Current methods of presenting the output of expert systems are centered mainly on the textual output of advice. This advice is generally predefined sentences embedded into the rule structure, often with some variables inserted at output. If a user queries such a system on the reason that particular advice was presented, additional textual output is presented. The explanation or in other words the basis for the advice is typically generated and presented one of two ways: 1) The system developer has hardcoded explanatory information or text into the system. This makes the explanation very local in nature, it does not present an explanation of the overall state of the expert system, and it is linked to the one particular rules it was written to explain. If the text is more global in scope, it makes it difficult to improve or add to the system in a modular way, because "end point" explanations must then be changed each time an intermediate rule explanation is changed. 2) The system recites a history of the inference chain that arrived at the current advice, often in reverse order. This is helpful in tracing any chain of rule firings, and can dynamically reflect new rules when they fire, but it tends to sound much like a series of "Why? Because. Why? Because..." responses, often very unsatisfying to the user.